Management analytics is a very broad term. It typically refers to applying different types of big data methods and analytical tools (techniques, strategies, and architectures) to different operation management topical areas, namely forecasting, inventory management, revenue management and marketing, transportation management, supply chain management, and risk analysis.[i]

 

Fundamental Data Metrics/KPIs

• Monthly financial (over/under) performance

• Employee salary cost

• Transaction summary (purchase, discounts, amounts, broken by time)

• Employee hours worked

• Machine hours usage

• Profit/Loss summary

• Asset productivity summary

• Employee number gain and lost

• Social media engagement summary

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Mission Critical Reports/Analysis [ii]

Reporting:

• Transaction report: over time, by product, over/underperformance

• Employee performance report

• Financial reporting, with important ratios, alerts on deviating costs/revenue

• Operations efficiency report: revenue/employee, profit/employee, profit margin, profit/machine

• Advertising performance and ROI

• Social media performance report

• Budget expectation and goal completion report

 

Advanced Metrics [iii]

Percentage of processes where completion falls within +/- 5% of the estimated completion, Average process overdue time, Percentage of overdue processes, Average process age, Percentage of processes where the actual number assigned resources is less than planned number of assigned resources, Sum of costs of “killed” / stopped active processes, Average time to complete task, Sum of deviation of time (e.g. in days) against planned schedule of all active project.

 

Advanced (Intermediate) Techniques [iv]

Meeting promoter score: tracks the usefulness and effectiveness of day-to-day meetings and can be used as a good proxy of management efficiency.

 

Application: Industry Examples [v]

Energy Management: Many firms are using big data for energy management, including energy optimization, smart-grid management, building automation and energy distribution in utility companies. The use case is centered around monitoring and controlling network devices, manage service outages, and dispatch crews. It gives utilities the ability to integrate millions of data points on network performance and lets engineers use analytics to monitor the network.

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Risk Management: Kaiser Permanente collects petabytes of health information on its 8-million-plus members. Some of this data was used in an FDA-sponsored study to identify risks with Vioxx, Merck’s pain medication, which was pulled shortly after the research identified a greater risk of heart attack in a subset of the patient population. According to Kaiser Permanente, “with our EHR system, we’ve become much more data driven and analytics oriented. Pretty much every actor in the care delivery system is using the same record and entering information in the same place. That allows us to do some remarkable things with regard to thinking about where and how members should receive care, and how to improve the flow of information, while at the same time lowering costs.”

 

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[i] Brett Massimino, John V. Gray and Yingchao Lan (2017). On the Inattention to Digital Confidentiality in Operations and Supply Chain Research, Production and Operations Management. Retrieved from here

[ii] Analytics Information Management. Deloitte. Retrieved from here

[iii] KPI – Key Performance Indicators. PNMSOFT. Retrieved from here

[iv] Upbin, Bruce (2011). Five New Management Metrics You Need To Know. Forbes. Retrieved from here

[v] Kalakota, Ravi (2015). Big Data Analytics Use Cases. Practical Analytics. Retrieved from here